Unfortunately, 3D data is challenging to analyze with CNNs, as traditional CNNs are designed to operate on the uniform, grid-like structure of an image. Various efforts have been made to process other forms of 3D and geometric data [1]}, [2]}, [3]}. The pioneering method for working directly on meshes is MeshCNN [4]}. MeshCNN rethinks the convolution and pooling operations to take advantage of the non-uniformity and irregularity of the mesh, thus outperforming previous methods on various tasks such as segmentation which operate indirectly on meshes or on other data formats such as point cloud.
